Using brain fMRI machine learning as a predictor of PTSD treatment outcomes for Canadian Military

Approximately 13% of Canadian Armed Forces (CAF) members and veterans deployed to Afghanistan are diagnosed with a deployment-related mental disorder, such as post-traumatic stress disorder (PTSD) and many will experience comorbid major depressive disorder. The proposed study will utilize brain imaging data (fMRI) to determine if neurobiological machine learning algorithms can predict treatment outcomes and psychiatric symptomatology in CAF members and veterans. This research will benefit CAF members and veterans through the identification and clinical application of novel avenues to personalized medicine; namely, using neuroimaging data to predict probable treatment outcomes and aid in the selection of appropriate treatment methodologies. The proposed research requires making data linkages and generalizing datasets by combining imaging and clinical outcomes data; however, by overcoming these challenges, we anticipate developing a tool that can aid in the diagnosis of PTSD and its various subtypes as well as inform treatment guidelines.